In addition, we compared ESIM [1]}, whose variants attained the best performance on the SWAG set on the release of the dataset [2]}.
In fact, ESIM specializes in classifying the relation of a sentence pair into one of given multi-class labels rather than estimating sentence probabilities.
Under the next sentence prediction circumstance, ESIM can be applied as a binary classifier to determine whether a preceding sentence and a candidate subsequent sentence are consistent.
To do so, we modified ESIM to be optimized with binary CE between the true label and \(1/(1+\nu \exp (-v))\) , where \(v\)  is the scalar output of the ESIM network.
